Package org.apache.commons.math3.filter

Examples of org.apache.commons.math3.filter.KalmanFilter

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        // the measurement covariance matrix -> put the "real" variance
        RealMatrix R = new Array2DRowRealMatrix(new double[] { measurementNoise * measurementNoise });

        final ProcessModel pm = new DefaultProcessModel(A, B, Q, x0, P0);
        final MeasurementModel mm = new DefaultMeasurementModel(H, R);
        final KalmanFilter filter = new KalmanFilter(pm, mm);

        final List<Number> xAxis = new ArrayList<Number>();
        final List<Number> realVoltageSeries = new ArrayList<Number>();
        final List<Number> measuredVoltageSeries = new ArrayList<Number>();
        final List<Number> kalmanVoltageSeries = new ArrayList<Number>();

        final List<Number> covSeries = new ArrayList<Number>();
       
        for (int i = 0; i < 300; i++) {
            xAxis.add(i);

            voltMeter.step();

            realVoltageSeries.add(voltMeter.getVoltage());

            // get the measured voltage from the volt meter
            final double measuredVoltage = voltMeter.getMeasuredVoltage();
            measuredVoltageSeries.add(measuredVoltage);

            kalmanVoltageSeries.add(filter.getStateEstimation()[0]);
            covSeries.add(filter.getErrorCovariance()[0][0]);

            filter.predict();
            filter.correct(new double[] { measuredVoltage });
        }

        chart1.setYAxisTitle("Voltage");
        chart1.setXAxisTitle("Iteration");

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            // build the P matrix elements from Taylor series formulas
            final BigFraction[] pI = pData[i];
            final int factor = -(i + 1);
            int aj = factor;
            for (int j = 0; j < pI.length; ++j) {
                pI[j] = new BigFraction(aj * (j + 2));
                aj *= factor;
            }
        }

        return new Array2DRowFieldMatrix<BigFraction>(pData, false);
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      List<SiteWithPolynomial> nearestSites =
          nearestSiteMap.get(site);
     
      RealVector vector = new ArrayRealVector(SITES_FOR_APPROX);
      RealMatrix matrix = new Array2DRowRealMatrix(
          SITES_FOR_APPROX, DefaultPolynomial.NUM_COEFFS);
     
      for (int row = 0; row < SITES_FOR_APPROX; row++) {
        SiteWithPolynomial nearSite = nearestSites.get(row);
        DefaultPolynomial.populateMatrix(matrix, row, nearSite.pos.x, nearSite.pos.z);
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        }

        // solve the rectangular system in the least square sense
        // to get the best estimate of the Nordsieck vector [s2 ... sk]
        QRDecomposition decomposition;
        decomposition = new QRDecomposition(new Array2DRowRealMatrix(a, false));
        RealMatrix x = decomposition.getSolver().solve(new Array2DRowRealMatrix(b, false));
        return new Array2DRowRealMatrix(x.getData(), false);
    }
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            // update Nordsieck vector
            final double[] predictedScaled = new double[y0.length];
            for (int j = 0; j < y0.length; ++j) {
                predictedScaled[j] = stepSize * yDot[j];
            }
            final Array2DRowRealMatrix nordsieckTmp = updateHighOrderDerivativesPhase1(nordsieck);
            updateHighOrderDerivativesPhase2(scaled, predictedScaled, nordsieckTmp);
            interpolator.reinitialize(stepEnd, stepSize, predictedScaled, nordsieckTmp);

            // discrete events handling
            interpolator.storeTime(stepEnd);
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     * @param residuals Residuals.
     * @return the cost.
     * @see #computeResiduals(double[])
     */
    protected double computeCost(double[] residuals) {
        final ArrayRealVector r = new ArrayRealVector(residuals);
        return FastMath.sqrt(r.dotProduct(getWeight().operate(r)));
    }
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    for (SiteWithPolynomial site : sites) {
     
      List<SiteWithPolynomial> nearestSites =
          nearestSiteMap.get(site);
     
      RealVector vector = new ArrayRealVector(SITES_FOR_APPROX);
      RealMatrix matrix = new Array2DRowRealMatrix(
          SITES_FOR_APPROX, DefaultPolynomial.NUM_COEFFS);
     
      for (int row = 0; row < SITES_FOR_APPROX; row++) {
        SiteWithPolynomial nearSite = nearestSites.get(row);
        DefaultPolynomial.populateMatrix(matrix, row, nearSite.pos.x, nearSite.pos.z);
        vector.setEntry(row, nearSite.pos.y);
      }
     
      QRDecomposition qr = new QRDecomposition(matrix);
      RealVector solution = qr.getSolver().solve(vector);
       
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    /**
     * @return a comparator for sorting the optima.
     */
    private Comparator<PointVectorValuePair> getPairComparator() {
        return new Comparator<PointVectorValuePair>() {
            private final RealVector target = new ArrayRealVector(optimizer.getTarget(), false);
            private final RealMatrix weight = optimizer.getWeight();

            public int compare(final PointVectorValuePair o1,
                               final PointVectorValuePair o2) {
                if (o1 == null) {
                    return (o2 == null) ? 0 : 1;
                } else if (o2 == null) {
                    return -1;
                }
                return Double.compare(weightedResidual(o1),
                                      weightedResidual(o2));
            }

            private double weightedResidual(final PointVectorValuePair pv) {
                final RealVector v = new ArrayRealVector(pv.getValueRef(), false);
                final RealVector r = target.subtract(v);
                return r.dotProduct(weight.operate(r));
            }
        };
    }
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     * @param residuals Residuals.
     * @return the cost.
     * @see #computeResiduals(double[])
     */
    protected double computeCost(double[] residuals) {
        final ArrayRealVector r = new ArrayRealVector(residuals);
        return FastMath.sqrt(r.dotProduct(getWeight().operate(r)));
    }
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     * @param matrix matrix with columns representing variables to correlate
     * @return correlation matrix
     */
    public RealMatrix computeCorrelationMatrix(final RealMatrix matrix) {
        int nVars = matrix.getColumnDimension();
        RealMatrix outMatrix = new BlockRealMatrix(nVars, nVars);
        for (int i = 0; i < nVars; i++) {
            for (int j = 0; j < i; j++) {
                double corr = correlation(matrix.getColumn(i), matrix.getColumn(j));
                outMatrix.setEntry(i, j, corr);
                outMatrix.setEntry(j, i, corr);
            }
            outMatrix.setEntry(i, i, 1d);
        }
        return outMatrix;
    }
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Related Classes of org.apache.commons.math3.filter.KalmanFilter

  • org.apache.commons.math3.analysis.differentiation.DerivativeStructure
  • org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
  • org.apache.commons.math3.analysis.function.HarmonicOscillator
  • org.apache.commons.math3.analysis.function.Sin
  • org.apache.commons.math3.analysis.function.Sinc
  • org.apache.commons.math3.analysis.MultivariateFunction
  • org.apache.commons.math3.analysis.QuinticFunction
  • org.apache.commons.math3.analysis.solvers.BrentSolver
  • org.apache.commons.math3.analysis.UnivariateFunction
  • org.apache.commons.math3.complex.Complex
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